Bottom Line:
Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits.However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants.The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

fig03: Manhattan plots summarising association of seven diseases from the WTCCC experiment with accumulations of well-imputed rare variants (MAF < 1% and info score of at least 0.4) within genes (as defined by the UCSC human genome database). Each point represents a gene, plotted according to the observed −log10P-value of association (y-axis) and the physical position of the midpoint of the transcript (x-axis), with those achieving genome-wide significance (P < 1.7 × 10−6) highlighted in red.

Mentions:
Figure 3 presents Manhattan plots to summarise the association of each disease with accumulations of minor alleles at well-imputed rare variants within genes, after correction for the three axes of genetic variation as covariates in the logistic regression model. In these Manhattan plots, each point represents a gene (as defined by the UCSC human genome database), and those achieving genome-wide significance (Bonferroni correction for 30,000 genes, P < 1.7 × 10−6) are highlighted in red. There was no evidence of residual population structure, not accounted for by the three axes of genetic variation, with genomic control inflation factors [Devlin and Roeder, 1999 less than one for all seven diseases (Supporting Information Figure S3).

fig03: Manhattan plots summarising association of seven diseases from the WTCCC experiment with accumulations of well-imputed rare variants (MAF < 1% and info score of at least 0.4) within genes (as defined by the UCSC human genome database). Each point represents a gene, plotted according to the observed −log10P-value of association (y-axis) and the physical position of the midpoint of the transcript (x-axis), with those achieving genome-wide significance (P < 1.7 × 10−6) highlighted in red.

Mentions:
Figure 3 presents Manhattan plots to summarise the association of each disease with accumulations of minor alleles at well-imputed rare variants within genes, after correction for the three axes of genetic variation as covariates in the logistic regression model. In these Manhattan plots, each point represents a gene (as defined by the UCSC human genome database), and those achieving genome-wide significance (Bonferroni correction for 30,000 genes, P < 1.7 × 10−6) are highlighted in red. There was no evidence of residual population structure, not accounted for by the three axes of genetic variation, with genomic control inflation factors [Devlin and Roeder, 1999 less than one for all seven diseases (Supporting Information Figure S3).

Bottom Line:
Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits.However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants.The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.